Efficiency of autocoding programs for converting job descriptors into standard occupational classification (SOC) codes

Abstract

Background: Existing datasets often lack job exposure data. Standard Occupational Classification (SOC) codes can link work exposure data to health outcomes via a Job Exposure Matrix, but manually assigning SOC codes is laborious. We explored the utility of two SOC autocoding programs.

Methods: We entered industry and occupation descriptions from two existing cohorts into two publicly available SOC autocoding programs. SOC codes were also assigned manually by experienced coders. These SOC codes were then linked to exposures from the Occupational Information Network (O*NET).

Results: Agreement between the SOC codes produced by autocoding programs and those produced manually was modest at the 6-digit level, and strong at the 2-digit level. Importantly, O*NET exposure values based on SOC code assignment showed strong agreement between manual and autocoded methods.

Conclusion: Both available autocoding programs can be useful tools for assigning SOC codes, allowing linkage of occupational exposures to data containing free-text occupation descriptors.

Keywords: NIOCCS; O*NET; SOCcer; industry and occupation coding; job exposure matrix.

Figure 1:

Agreement Between Autocoded and Manually Coded SOC Codes Based on Minimum Allowed Confidence Scores From Pooled Upper Extremity Study (Cohort 1), United States, 2001 – 2010, and From the Show-ME Study (Cohort 2), Missouri, 2012 – 2013 NIOCCS (NIOSH Industry and Occupation Computerized Coding System); SOC (Standard Occupational Classification); SOCcer (Standardized Occupation Coding for Computer-assisted Epidemiological Research) ^a SOCcer scores range from 0–1 ^b NIOCCS scores range from 90–100 (SOC codes with scores less than 90 are omitted by the program) ^c SOCcer and NIOCCS scores are aligned based on the proportion of the cohort that is autocoded at each minimum threshold Industry codes and occupation text included for both autocoding programs